Object Detection System with OpenCV and Python

Object Detection System with OpenCV and Python

Object Detection System with OpenCV and Python

Project Overview

Object detection means finding and locating objects in images or videos. OpenCV makes it easy to do this using Haar Cascades, which is a type of classifier trained with positive and negative images. In this article, we’ll learn how to detect objects using Haar Cascades. It’s a simple and effective way for real-time object detection. But before that, let’s first understand what Haar Cascades actually are.

This project is intended for educational and learning purposes.

Project Information

Project Name Language Used Developer
YOLO Object Detection with OpenCV Python UPDATEGADH

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 Installation Instructions

Install the required Python libraries before running the script:

pip install numpy
pip install opencv-python
pip install imutils

 Technology Stack

  • Programming Language: Python
  • Libraries Used: OpenCV, NumPy, Imutils
  • Model: YOLOv3 (Pre-trained on COCO dataset)
  • No Database Required

 Available Features

 1. Object detection using YOLOv3
 2. Detection in still images
 3. Supports 80+ COCO object classes
 4. Lightweight and fast
 Offline execution
 5. Easy to customize and extend
 Clean, modular Python code

 Limitations

  • Less effective for small objects or objects placed closely together.
  • Based on YOLO’s grid prediction system, which may skip multiple small objects in one grid cell.
  • Consider Faster R-CNN or SSD for use cases involving small or densely packed objects.

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